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  {
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   "source": [
    "# 用于预训练词嵌入的数据集\n",
    ":label:`sec_word2vec_data`\n",
    "\n",
    "现在我们已经了解了word2vec模型的技术细节和大致的训练方法，让我们来看看它们的实现。具体地说，我们将以 :numref:`sec_word2vec`的跳元模型和 :numref:`sec_approx_train`的负采样为例。本节从用于预训练词嵌入模型的数据集开始：数据的原始格式将被转换为可以在训练期间迭代的小批量。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "596ed133",
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    "execution": {
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    },
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    "tab": [
     "pytorch"
    ]
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   "source": [
    "import math\n",
    "import os\n",
    "import random\n",
    "import torch\n",
    "from d2l import torch as d2l"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8286adf0",
   "metadata": {
    "origin_pos": 4
   },
   "source": [
    "## 读取数据集\n",
    "\n",
    "我们在这里使用的数据集是[Penn Tree Bank（PTB）](https://catalog.ldc.upenn.edu/LDC99T42)。该语料库取自“华尔街日报”的文章，分为训练集、验证集和测试集。在原始格式中，文本文件的每一行表示由空格分隔的一句话。在这里，我们将每个单词视为一个词元。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cc6c9b2e",
   "metadata": {
    "execution": {
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    "tab": [
     "pytorch"
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading ../data/ptb.zip from http://d2l-data.s3-accelerate.amazonaws.com/ptb.zip...\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'# sentences数: 42069'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#@save\n",
    "d2l.DATA_HUB['ptb'] = (d2l.DATA_URL + 'ptb.zip',\n",
    "                       '319d85e578af0cdc590547f26231e4e31cdf1e42')\n",
    "\n",
    "#@save\n",
    "def read_ptb():\n",
    "    \"\"\"将PTB数据集加载到文本行的列表中\"\"\"\n",
    "    data_dir = d2l.download_extract('ptb')\n",
    "    # Readthetrainingset.\n",
    "    with open(os.path.join(data_dir, 'ptb.train.txt')) as f:\n",
    "        raw_text = f.read()\n",
    "    return [line.split() for line in raw_text.split('\\n')]\n",
    "\n",
    "sentences = read_ptb()\n",
    "f'# sentences数: {len(sentences)}'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7290de5",
   "metadata": {
    "origin_pos": 6
   },
   "source": [
    "在读取训练集之后，我们为语料库构建了一个词表，其中出现次数少于10次的任何单词都将由“&lt;unk&gt;”词元替换。请注意，原始数据集还包含表示稀有（未知）单词的“&lt;unk&gt;”词元。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "04285c2d",
   "metadata": {
    "execution": {
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    },
    "origin_pos": 7,
    "tab": [
     "pytorch"
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'vocab size: 6719'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vocab = d2l.Vocab(sentences, min_freq=10)\n",
    "f'vocab size: {len(vocab)}'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bba2291",
   "metadata": {
    "origin_pos": 8
   },
   "source": [
    "## 下采样\n",
    "\n",
    "文本数据通常有“the”“a”和“in”等高频词：它们在非常大的语料库中甚至可能出现数十亿次。然而，这些词经常在上下文窗口中与许多不同的词共同出现，提供的有用信息很少。例如，考虑上下文窗口中的词“chip”：直观地说，它与低频单词“intel”的共现比与高频单词“a”的共现在训练中更有用。此外，大量（高频）单词的训练速度很慢。因此，当训练词嵌入模型时，可以对高频单词进行*下采样* :cite:`Mikolov.Sutskever.Chen.ea.2013`。具体地说，数据集中的每个词$w_i$将有概率地被丢弃\n",
    "\n",
    "$$ P(w_i) = \\max\\left(1 - \\sqrt{\\frac{t}{f(w_i)}}, 0\\right),$$\n",
    "\n",
    "其中$f(w_i)$是$w_i$的词数与数据集中的总词数的比率，常量$t$是超参数（在实验中为$10^{-4}$）。我们可以看到，只有当相对比率$f(w_i) > t$时，（高频）词$w_i$才能被丢弃，且该词的相对比率越高，被丢弃的概率就越大。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "88d0f9c2",
   "metadata": {
    "execution": {
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   "source": [
    "#@save\n",
    "def subsample(sentences, vocab):\n",
    "    \"\"\"下采样高频词\"\"\"\n",
    "    # 排除未知词元'<unk>'\n",
    "    sentences = [[token for token in line if vocab[token] != vocab.unk]\n",
    "                 for line in sentences]\n",
    "    counter = d2l.count_corpus(sentences)\n",
    "    num_tokens = sum(counter.values())\n",
    "\n",
    "    # 如果在下采样期间保留词元，则返回True\n",
    "    def keep(token):\n",
    "        return(random.uniform(0, 1) <\n",
    "               math.sqrt(1e-4 / counter[token] * num_tokens))\n",
    "\n",
    "    return ([[token for token in line if keep(token)] for line in sentences],\n",
    "            counter)\n",
    "\n",
    "subsampled, counter = subsample(sentences, vocab)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c892ade",
   "metadata": {
    "origin_pos": 10
   },
   "source": [
    "下面的代码片段绘制了下采样前后每句话的词元数量的直方图。正如预期的那样，下采样通过删除高频词来显著缩短句子，这将使训练加速。\n"
   ]
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   "source": [
    "d2l.show_list_len_pair_hist(\n",
    "    ['origin', 'subsampled'], '# tokens per sentence',\n",
    "    'count', sentences, subsampled);"
   ]
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   "metadata": {
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   },
   "source": [
    "对于单个词元，高频词“the”的采样率不到1/20。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2ac63b1a",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2023-08-18T07:01:44.317982Z"
    },
    "origin_pos": 13,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\"the\"的数量：之前=50770, 之后=2056'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def compare_counts(token):\n",
    "    return (f'\"{token}\"的数量：'\n",
    "            f'之前={sum([l.count(token) for l in sentences])}, '\n",
    "            f'之后={sum([l.count(token) for l in subsampled])}')\n",
    "\n",
    "compare_counts('the')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73ef69ef",
   "metadata": {
    "origin_pos": 14
   },
   "source": [
    "相比之下，低频词“join”则被完全保留。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9307cb04",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T07:01:44.324650Z",
     "iopub.status.busy": "2023-08-18T07:01:44.323726Z",
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     "shell.execute_reply": "2023-08-18T07:01:44.365449Z"
    },
    "origin_pos": 15,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\"join\"的数量：之前=45, 之后=45'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare_counts('join')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38762200",
   "metadata": {
    "origin_pos": 16
   },
   "source": [
    "在下采样之后，我们将词元映射到它们在语料库中的索引。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ed59e4d0",
   "metadata": {
    "execution": {
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     "iopub.status.busy": "2023-08-18T07:01:44.370695Z",
     "iopub.status.idle": "2023-08-18T07:01:44.930927Z",
     "shell.execute_reply": "2023-08-18T07:01:44.929824Z"
    },
    "origin_pos": 17,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[], [2115, 274, 406], [140, 3, 5277, 3054, 1580]]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corpus = [vocab[line] for line in subsampled]\n",
    "corpus[:3]"
   ]
  },
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   "cell_type": "markdown",
   "id": "fe5918fb",
   "metadata": {
    "origin_pos": 18
   },
   "source": [
    "## 中心词和上下文词的提取\n",
    "\n",
    "下面的`get_centers_and_contexts`函数从`corpus`中提取所有中心词及其上下文词。它随机采样1到`max_window_size`之间的整数作为上下文窗口。对于任一中心词，与其距离不超过采样上下文窗口大小的词为其上下文词。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d4a20ba3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T07:01:44.935833Z",
     "iopub.status.busy": "2023-08-18T07:01:44.935066Z",
     "iopub.status.idle": "2023-08-18T07:01:44.944963Z",
     "shell.execute_reply": "2023-08-18T07:01:44.943901Z"
    },
    "origin_pos": 19,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "#@save\n",
    "def get_centers_and_contexts(corpus, max_window_size):\n",
    "    \"\"\"返回跳元模型中的中心词和上下文词\"\"\"\n",
    "    centers, contexts = [], []\n",
    "    for line in corpus:\n",
    "        # 要形成“中心词-上下文词”对，每个句子至少需要有2个词\n",
    "        if len(line) < 2:\n",
    "            continue\n",
    "        centers += line\n",
    "        for i in range(len(line)):  # 上下文窗口中间i\n",
    "            window_size = random.randint(1, max_window_size)\n",
    "            indices = list(range(max(0, i - window_size),\n",
    "                                 min(len(line), i + 1 + window_size)))\n",
    "            # 从上下文词中排除中心词\n",
    "            indices.remove(i)\n",
    "            contexts.append([line[idx] for idx in indices])\n",
    "    return centers, contexts"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86fba895",
   "metadata": {
    "origin_pos": 20
   },
   "source": [
    "接下来，我们创建一个人工数据集，分别包含7个和3个单词的两个句子。设置最大上下文窗口大小为2，并打印所有中心词及其上下文词。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fae4771b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T07:01:44.948910Z",
     "iopub.status.busy": "2023-08-18T07:01:44.948190Z",
     "iopub.status.idle": "2023-08-18T07:01:44.955563Z",
     "shell.execute_reply": "2023-08-18T07:01:44.954488Z"
    },
    "origin_pos": 21,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据集 [[0, 1, 2, 3, 4, 5, 6], [7, 8, 9]]\n",
      "中心词 0 的上下文词是 [1]\n",
      "中心词 1 的上下文词是 [0, 2]\n",
      "中心词 2 的上下文词是 [0, 1, 3, 4]\n",
      "中心词 3 的上下文词是 [2, 4]\n",
      "中心词 4 的上下文词是 [3, 5]\n",
      "中心词 5 的上下文词是 [4, 6]\n",
      "中心词 6 的上下文词是 [5]\n",
      "中心词 7 的上下文词是 [8, 9]\n",
      "中心词 8 的上下文词是 [7, 9]\n",
      "中心词 9 的上下文词是 [7, 8]\n"
     ]
    }
   ],
   "source": [
    "tiny_dataset = [list(range(7)), list(range(7, 10))]\n",
    "print('数据集', tiny_dataset)\n",
    "for center, context in zip(*get_centers_and_contexts(tiny_dataset, 2)):\n",
    "    print('中心词', center, '的上下文词是', context)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e21272fc",
   "metadata": {
    "origin_pos": 22
   },
   "source": [
    "在PTB数据集上进行训练时，我们将最大上下文窗口大小设置为5。下面提取数据集中的所有中心词及其上下文词。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ec92f27e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T07:01:44.960145Z",
     "iopub.status.busy": "2023-08-18T07:01:44.959231Z",
     "iopub.status.idle": "2023-08-18T07:01:47.218796Z",
     "shell.execute_reply": "2023-08-18T07:01:47.217626Z"
    },
    "origin_pos": 23,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'# “中心词-上下文词对”的数量: 1499984'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_centers, all_contexts = get_centers_and_contexts(corpus, 5)\n",
    "f'# “中心词-上下文词对”的数量: {sum([len(contexts) for contexts in all_contexts])}'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f48c535f",
   "metadata": {
    "origin_pos": 24
   },
   "source": [
    "## 负采样\n",
    "\n",
    "我们使用负采样进行近似训练。为了根据预定义的分布对噪声词进行采样，我们定义以下`RandomGenerator`类，其中（可能未规范化的）采样分布通过变量`sampling_weights`传递。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "365189a2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T07:01:47.223801Z",
     "iopub.status.busy": "2023-08-18T07:01:47.223354Z",
     "iopub.status.idle": "2023-08-18T07:01:47.232254Z",
     "shell.execute_reply": "2023-08-18T07:01:47.231166Z"
    },
    "origin_pos": 25,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "#@save\n",
    "class RandomGenerator:\n",
    "    \"\"\"根据n个采样权重在{1,...,n}中随机抽取\"\"\"\n",
    "    def __init__(self, sampling_weights):\n",
    "        # Exclude\n",
    "        self.population = list(range(1, len(sampling_weights) + 1))\n",
    "        self.sampling_weights = sampling_weights\n",
    "        self.candidates = []\n",
    "        self.i = 0\n",
    "\n",
    "    def draw(self):\n",
    "        if self.i == len(self.candidates):\n",
    "            # 缓存k个随机采样结果\n",
    "            self.candidates = random.choices(\n",
    "                self.population, self.sampling_weights, k=10000)\n",
    "            self.i = 0\n",
    "        self.i += 1\n",
    "        return self.candidates[self.i - 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f886ada9",
   "metadata": {
    "origin_pos": 26
   },
   "source": [
    "例如，我们可以在索引1、2和3中绘制10个随机变量$X$，采样概率为$P(X=1)=2/9, P(X=2)=3/9$和$P(X=3)=4/9$，如下所示。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f534865c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T07:01:47.237153Z",
     "iopub.status.busy": "2023-08-18T07:01:47.236381Z",
     "iopub.status.idle": "2023-08-18T07:01:47.251510Z",
     "shell.execute_reply": "2023-08-18T07:01:47.250435Z"
    },
    "origin_pos": 27,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 2, 3, 3, 3, 3, 2, 1, 2]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#@save\n",
    "generator = RandomGenerator([2, 3, 4])\n",
    "[generator.draw() for _ in range(10)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe4049d4",
   "metadata": {
    "origin_pos": 28
   },
   "source": [
    "对于一对中心词和上下文词，我们随机抽取了`K`个（实验中为5个）噪声词。根据word2vec论文中的建议，将噪声词$w$的采样概率$P(w)$设置为其在字典中的相对频率，其幂为0.75 :cite:`Mikolov.Sutskever.Chen.ea.2013`。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "21950025",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T07:01:47.256344Z",
     "iopub.status.busy": "2023-08-18T07:01:47.255586Z",
     "iopub.status.idle": "2023-08-18T07:01:59.259799Z",
     "shell.execute_reply": "2023-08-18T07:01:59.258793Z"
    },
    "origin_pos": 29,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "#@save\n",
    "def get_negatives(all_contexts, vocab, counter, K):\n",
    "    \"\"\"返回负采样中的噪声词\"\"\"\n",
    "    # 索引为1、2、...（索引0是词表中排除的未知标记）\n",
    "    sampling_weights = [counter[vocab.to_tokens(i)]**0.75\n",
    "                        for i in range(1, len(vocab))]\n",
    "    all_negatives, generator = [], RandomGenerator(sampling_weights)\n",
    "    for contexts in all_contexts:\n",
    "        negatives = []\n",
    "        while len(negatives) < len(contexts) * K:\n",
    "            neg = generator.draw()\n",
    "            # 噪声词不能是上下文词\n",
    "            if neg not in contexts:\n",
    "                negatives.append(neg)\n",
    "        all_negatives.append(negatives)\n",
    "    return all_negatives\n",
    "\n",
    "all_negatives = get_negatives(all_contexts, vocab, counter, 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8aa17e2d",
   "metadata": {
    "origin_pos": 30
   },
   "source": [
    "## 小批量加载训练实例\n",
    ":label:`subsec_word2vec-minibatch-loading`\n",
    "\n",
    "在提取所有中心词及其上下文词和采样噪声词后，将它们转换成小批量的样本，在训练过程中可以迭代加载。\n",
    "\n",
    "在小批量中，$i^\\mathrm{th}$个样本包括中心词及其$n_i$个上下文词和$m_i$个噪声词。由于上下文窗口大小不同，$n_i+m_i$对于不同的$i$是不同的。因此，对于每个样本，我们在`contexts_negatives`个变量中将其上下文词和噪声词连结起来，并填充零，直到连结长度达到$\\max_i n_i+m_i$(`max_len`)。为了在计算损失时排除填充，我们定义了掩码变量`masks`。在`masks`中的元素和`contexts_negatives`中的元素之间存在一一对应关系，其中`masks`中的0（否则为1）对应于`contexts_negatives`中的填充。\n",
    "\n",
    "为了区分正反例，我们在`contexts_negatives`中通过一个`labels`变量将上下文词与噪声词分开。类似于`masks`，在`labels`中的元素和`contexts_negatives`中的元素之间也存在一一对应关系，其中`labels`中的1（否则为0）对应于`contexts_negatives`中的上下文词的正例。\n",
    "\n",
    "上述思想在下面的`batchify`函数中实现。其输入`data`是长度等于批量大小的列表，其中每个元素是由中心词`center`、其上下文词`context`和其噪声词`negative`组成的样本。此函数返回一个可以在训练期间加载用于计算的小批量，例如包括掩码变量。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8e92a65e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T07:01:59.264970Z",
     "iopub.status.busy": "2023-08-18T07:01:59.264337Z",
     "iopub.status.idle": "2023-08-18T07:01:59.271417Z",
     "shell.execute_reply": "2023-08-18T07:01:59.270518Z"
    },
    "origin_pos": 31,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "#@save\n",
    "def batchify(data):\n",
    "    \"\"\"返回带有负采样的跳元模型的小批量样本\"\"\"\n",
    "    max_len = max(len(c) + len(n) for _, c, n in data)\n",
    "    centers, contexts_negatives, masks, labels = [], [], [], []\n",
    "    for center, context, negative in data:\n",
    "        cur_len = len(context) + len(negative)\n",
    "        centers += [center]\n",
    "        contexts_negatives += \\\n",
    "            [context + negative + [0] * (max_len - cur_len)]\n",
    "        masks += [[1] * cur_len + [0] * (max_len - cur_len)]\n",
    "        labels += [[1] * len(context) + [0] * (max_len - len(context))]\n",
    "    return (torch.tensor(centers).reshape((-1, 1)), torch.tensor(\n",
    "        contexts_negatives), torch.tensor(masks), torch.tensor(labels))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7aeb5c51",
   "metadata": {
    "origin_pos": 32
   },
   "source": [
    "让我们使用一个小批量的两个样本来测试此函数。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e14e34ce",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T07:01:59.276193Z",
     "iopub.status.busy": "2023-08-18T07:01:59.275387Z",
     "iopub.status.idle": "2023-08-18T07:01:59.282832Z",
     "shell.execute_reply": "2023-08-18T07:01:59.281912Z"
    },
    "origin_pos": 33,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "centers = tensor([[1],\n",
      "        [1]])\n",
      "contexts_negatives = tensor([[2, 2, 3, 3, 3, 3],\n",
      "        [2, 2, 2, 3, 3, 0]])\n",
      "masks = tensor([[1, 1, 1, 1, 1, 1],\n",
      "        [1, 1, 1, 1, 1, 0]])\n",
      "labels = tensor([[1, 1, 0, 0, 0, 0],\n",
      "        [1, 1, 1, 0, 0, 0]])\n"
     ]
    }
   ],
   "source": [
    "x_1 = (1, [2, 2], [3, 3, 3, 3])\n",
    "x_2 = (1, [2, 2, 2], [3, 3])\n",
    "batch = batchify((x_1, x_2))\n",
    "\n",
    "names = ['centers', 'contexts_negatives', 'masks', 'labels']\n",
    "for name, data in zip(names, batch):\n",
    "    print(name, '=', data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1eef3d8",
   "metadata": {
    "origin_pos": 34
   },
   "source": [
    "## 整合代码\n",
    "\n",
    "最后，我们定义了读取PTB数据集并返回数据迭代器和词表的`load_data_ptb`函数。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8ddfb20d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T07:01:59.287587Z",
     "iopub.status.busy": "2023-08-18T07:01:59.286823Z",
     "iopub.status.idle": "2023-08-18T07:01:59.296040Z",
     "shell.execute_reply": "2023-08-18T07:01:59.294978Z"
    },
    "origin_pos": 36,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "#@save\n",
    "def load_data_ptb(batch_size, max_window_size, num_noise_words):\n",
    "    \"\"\"下载PTB数据集，然后将其加载到内存中\"\"\"\n",
    "    num_workers = d2l.get_dataloader_workers()\n",
    "    sentences = read_ptb()\n",
    "    vocab = d2l.Vocab(sentences, min_freq=10)\n",
    "    subsampled, counter = subsample(sentences, vocab)\n",
    "    corpus = [vocab[line] for line in subsampled]\n",
    "    all_centers, all_contexts = get_centers_and_contexts(\n",
    "        corpus, max_window_size)\n",
    "    all_negatives = get_negatives(\n",
    "        all_contexts, vocab, counter, num_noise_words)\n",
    "\n",
    "    class PTBDataset(torch.utils.data.Dataset):\n",
    "        def __init__(self, centers, contexts, negatives):\n",
    "            assert len(centers) == len(contexts) == len(negatives)\n",
    "            self.centers = centers\n",
    "            self.contexts = contexts\n",
    "            self.negatives = negatives\n",
    "\n",
    "        def __getitem__(self, index):\n",
    "            return (self.centers[index], self.contexts[index],\n",
    "                    self.negatives[index])\n",
    "\n",
    "        def __len__(self):\n",
    "            return len(self.centers)\n",
    "\n",
    "    dataset = PTBDataset(all_centers, all_contexts, all_negatives)\n",
    "\n",
    "    data_iter = torch.utils.data.DataLoader(\n",
    "        dataset, batch_size, shuffle=True,\n",
    "        collate_fn=batchify, num_workers=num_workers)\n",
    "    return data_iter, vocab"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97991d10",
   "metadata": {
    "origin_pos": 38
   },
   "source": [
    "让我们打印数据迭代器的第一个小批量。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5115b257",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T07:01:59.300574Z",
     "iopub.status.busy": "2023-08-18T07:01:59.299960Z",
     "iopub.status.idle": "2023-08-18T07:02:13.672095Z",
     "shell.execute_reply": "2023-08-18T07:02:13.671142Z"
    },
    "origin_pos": 39,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "centers shape: torch.Size([512, 1])\n",
      "contexts_negatives shape: torch.Size([512, 60])\n",
      "masks shape: torch.Size([512, 60])\n",
      "labels shape: torch.Size([512, 60])\n"
     ]
    }
   ],
   "source": [
    "data_iter, vocab = load_data_ptb(512, 5, 5)\n",
    "for batch in data_iter:\n",
    "    for name, data in zip(names, batch):\n",
    "        print(name, 'shape:', data.shape)\n",
    "    break"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfc03f54",
   "metadata": {
    "origin_pos": 40
   },
   "source": [
    "## 小结\n",
    "\n",
    "* 高频词在训练中可能不是那么有用。我们可以对他们进行下采样，以便在训练中加快速度。\n",
    "* 为了提高计算效率，我们以小批量方式加载样本。我们可以定义其他变量来区分填充标记和非填充标记，以及正例和负例。\n",
    "\n",
    "## 练习\n",
    "\n",
    "1. 如果不使用下采样，本节中代码的运行时间会发生什么变化？\n",
    "1. `RandomGenerator`类缓存`k`个随机采样结果。将`k`设置为其他值，看看它如何影响数据加载速度。\n",
    "1. 本节代码中的哪些其他超参数可能会影响数据加载速度？\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e415387",
   "metadata": {
    "origin_pos": 42,
    "tab": [
     "pytorch"
    ]
   },
   "source": [
    "[Discussions](https://discuss.d2l.ai/t/5735)\n"
   ]
  }
 ],
 "metadata": {
  "language_info": {
   "name": "python"
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  "required_libs": []
 },
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 "nbformat_minor": 5
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